Data Analysis Report Paper Sample.

**Data Analysis**

**Hypothesis**

The increased military tension (military expenditure as % of GDP) in Iraq between 1990 and 2000 resulted in an increase in the trade tension (GDP) of the country.

**Dependent variable**

The dependent variable for the research is the trade tension. It will be measured as the Gross Domestic Product (GDP) of Iraq for the period between 1990 and 2000. Hence, the trade tension is conceptualized as the decrement in trade activities or progressive deterioration of the economic sector due to impaired exchange of goods and services. As aforementioned, the dependent variable will be operationalized and considered and being mutually exclusive to the annual GDP value for the studied country over the chosen study period. In this case, the level of measurement that will be used to estimate the trade tension will be the ratio since the GDP will be measured as an amount in dollars. The data will be harnessed from the World Trade Organization database for the period 1991-2000.

**Independent variable**

The independent variable for the research will be military tension, whose trend between 1991 and 2000 will be estimated through measuring the variance of the military expenditure as a percentage of the GDP for Iraq. For this variable, the conceptualization can be termed to be that military tension is the restlessness produced between two or more parties due to increased soldierly-related activities within one of the party’s region. Consequently, it the operationalization for this independent variable will be illustrated by evaluating the fluctuations caused of the military expenditure of Iraq as a percentage of the GDP over the period of measurement. Notably, the level of measurement for the independent variable will be the ratio because, as indicated by the name, the value of the military spending as a percentage of the GDP expressed as a percent. To get the ratio, the military expenditure shall be divided by the GDP for the same annual period and the result multiplied by 100%. The data used for measuring the independent variable will be harvested from a website called indexmundi.com, which has credible defense and arms trade date that indicates the military expenditure for Iraq over the period between 1990 and 2000.

**Data: 3, 4, 5, 5, 6, 6, 6, 7, 8, 20**- Mean

Mean is the average of the give set of figures given by the formula:

= (3 + 4 + 5 + 5 + 6 + 6 + 6 + 7 + 8 + 20)/10

= 7

- Median

The number of terms is even. Hence,

Median = [(6 + 6)/2] – 1

= 5

- Mode

The number that appears most in the set is 6, hence

Mode = 6

**Standard error**

σ =

Where X_{i }= each value of the dataset

∑ (X_{i }– x̄)^{2 }= [(3 – 7)^{ 2} + (4 – 7)^{ 2} + (5 – 7)^{ 2} + (5 – 7)^{ 2} + (6 – 7)^{ 2} + (6 – 7)^{ 2} + (6 – 7)^{ 2} + (7 – 7)^{ 2} + (8 – 7)^{ 2} + (20 – 7)^{2}

= (-4^{2} + -3^{2} + -2^{2} + -2^{2} + -1^{2} + -1^{2} + -1^{2} + -0^{2 }+ 1^{2} + 13^{2})

= 16 + 9 + 4 + 4 + 1 + 1 + 1 + 0 + 1 + 169

= 206

σ =

=

= 4.54

**Z score for a score equal or greater than 14**

Z =

Z = (14-7)/ (2*4.54)

= 7/ (2*4.54)

= 0.76 ± 1.96 at 95% confidence level

For 14 and above, confidence interval = 0.76-2.72

**Difference between type 1 and 2 errors**

Whereas Type 1 errors, also called false positive, are accrued when the researcher rejects a null hypothesis instead of accepting it and replacing it with an alternative hypothesis of interest, the type 2 errors refer to the errors that a person conducts when he/she fails to reject a false null hypothesis and instead substitutes it with one that seems to be true.

**The null hypothesis for question 1**

**H _{0}:** The increased military tension (military expenditure as % of GDP) in Iraq between 1990 and 2000 did not result in an increase in the trade tension (GDP) of the country.

**The confidence interval of number of the of students**

Number of students surveyed = 500

Number of students who took the class = 15

Confidence interval

At 95% CI, z = 1.96

**Confidence interval for average test**

Data: | |

Senior scores | 90, 93, 88, 85 |

Freshman scores | 82, 84, 81, 85 |

Mean:

Senior scores, = (90 + 93 + 88 + 85)/4

= 89

Freshman scores, = (82 + 84 + 81 + 85)/4

= 83

Standard deviation squared:

Senior scores, = [((85-89)^{2} + (88-89)^{2} + (90-89)^{2} + (93-89)^{2})/4])^{ 2}

= [(-4)^{2 }+ (-1)^{2} + (1)^{2 }+ (4)^{2})/ 4]}^{ 2}

= ((16+1+1+16)/4)}^{ 2}

= (2.916)^{ 2}

= 8.5

Freshman scores, = [((81-83)^{2} + (82-83)^{2} + (84-83)^{2} + (85-83)^{2})/4]}^{ 2}

= [(-2)^{2 }+ (-1)^{2} + (1)^{2 }+ (2)^{2})/ 4]}^{ 2}

= ((4+1+1+4)/4)}^{ 2}

= (1.581)^{ 2}

^{ }= 2.5

z score at 95% Confidence level = 1.96

Therefore, CI = (89 – 83) ^{2} 1.96 *

= 36 1.96 *

= 36 3.25

Hence, confidence intervals = 35.75 to 39.25 at 95% confidence level

**Statistical difference**

Data: | United States | Japan |

Did not vote | 40% | 33% |

Voted | 52% | 66% |

Did not answer | 8% | 1% |

Sample size | 200 | 200 |